tbuckley/cabot-search huggingface dataset:
Abstract 7: AGS3 depletion promotes tumorigenesis in a murine model of lung cancer
Authors: Timothy O. Adekoya, Nikia Smith, Ricardo M. Richardson
Journal: Cancer Research (2023)

Abstract Activator of G-protein Signaling 3 (AGS3/GPSM1) is a receptor independent activator of G-protein signaling that couples to Gα subunits of the heterotrimeric G-protein to mediate cellular functions. It was previously shown that AGS3 modulates prostate tumor development. In this study we investigate the role of AGS3 in the onset of lung cancer. To that end, we used a murine model of Lung Lewis Carcinoma (LLC) in mice deficient in AGS3 expression (AGS3-/-) relative to wild-type animals (AGS3+/+). AGS3-/- mice exhibited a significant increase in LLC tumor growth relative to AGS3+/+ mice. Immunohistochemical analysis of the tumors also revealed a significant increase in microvessels density in AGS3-/- tumors. FACS analysis of tumor infiltrating leukocytes also showed a significant decrease in lymphocyte recruitment in AGS3-/- tumor microenvironment (TME) relative to AGS3+/+ mice. The decrease leukocytes recruitment in AGS3-/- xenografts correlated with a marked decrease in chemokines expression in the TME. Taken together, these results indicate that AGS3 modulates LLC tumor development and progression. Citation Format: Timothy O. Adekoya, Nikia Smith, Ricardo M. Richardson. AGS3 depletion promotes tumorigenesis in a murine model of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 7.
Abstract 996: Identification of molecular biomarkers differentiating malignant uterine leiomyosarcoma from benign leiomyoma
Authors: Sara Khamaiseh, Riitta Koivisto-Korander, Terhi Ahvenainen, Ralf Bützow, Miika Mehine, Pia Vahteristo
Journal: Cancer Research (2023)

Abstract Uterine leiomyosarcomas are rare, aggressive cancers that represent the most common type of uterine sarcomas. They frequently harbor genetic alterations in TP53, RB1, PTEN, and ATRX. Their benign counterpart, uterine leiomyomas (or fibroids) are very common tumors that may significantly affect women’s quality of life. The majority of leiomyomas harbor genetic alterations affecting either MED12, HMGA2, or FH. A third of leiomyosarcomas have been reported to harbor leiomyoma-associated driver mutations, suggesting that some leiomyomas may have undergone malignant transformation. The leiomyoma/leiomyosarcoma diagnosis is largely based on histopathology. This can be specifically challenging in differentiating leiomyosarcomas from a subset of leiomyomas that display histopathological features resembling malignancy. The aim of this study was to identify molecular biomarkers that can accurately differentiate benign and malignant uterine smooth muscle tumors. 3’RNA sequencing of 48 leiomyosarcomas and 44 leiomyomas revealed that leiomyosarcomas are uniquely characterized by dysregulation of the retinoblastoma pathway. We confirmed upregulation of several members of this pathway, including TOP2A and CDK1, in a publicly available transcriptomic dataset of 49 uterine leiomyosarcomas. The second objective of this study was to explore the gene expression profile of leiomyosarcomas carrying leiomyoma-associated driver mutations. This revealed that unlike leiomyomas, leiomyosarcomas do not form distinct gene expression profiles based on the presence of leiomyoma-associated driver mutations such as MED12. However, leiomyosarcomas with a MED12 mutation showed upregulation of RAD51B and ADAM12, which we have previously highlighted as biomarkers of leiomyomas harboring a MED12 mutation. We identified upregulation of IRS4 and characteristic deletions affecting COL4A5 and COL4A6 in four in-house and three additional uterine leiomyosarcomas from The Cancer Genome Atlas (TCGA) collection. Similar driver alterations have previously been reported in a small subset of leiomyomas but not in leiomyosarcomas. In conclusion, leiomyosarcomas display a gene expression profile characterized by dysregulation of genes related to the retinoblastoma pathway. These include potential biomarkers that could be used in a clinical setting to differentiate leiomyosarcomas from leiomyomas. Citation Format: Sara Khamaiseh, Riitta Koivisto-Korander, Terhi Ahvenainen, Ralf Bützow, Miika Mehine, Pia Vahteristo. Identification of molecular biomarkers differentiating malignant uterine leiomyosarcoma from benign leiomyoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 996.
Abstract 3242: Efficacy evaluation by novel humanized mouse models for preclinical study of ADCs combined with immunotherapy
Authors: Fang Zhu, Hongyan Sun, Qian Lu, Yuan Fang, Jianming Xu, Cunxiang Ju, Hongyu Wang, Santi Suryani Chen, Zhiying Li, Mark Wade Moore, Jing Zhao, Xiang Gao
Journal: Cancer Research (2023)

Abstract Antibody-drug conjugates (ADCs) are effective anti-tumor therapies with high specificity, with little toxicity and side effects. Xenograft tumor models based on immunodeficient mice have been used for evaluating the human anti-tumor efficacy of ADCs. However, the antibody-mediated immune response of ADCs is not well evaluated in immunodeficient mice. To this end, we have developed BALB/c-hPD1 humanized mouse model to evaluate the efficacy of ADCs in combination with immunotherapy. Trastuzumab deruxtecan (T-DXd, DS-8201a) is a HER2-targeting ADC. In vitro efficacy studies showed that T-DXd effectively bound to the HER2-positive cell line SK-BR-3, resulting in strong endocytosis and induction of apoptosis. It has also been shown to kill HER2-negative MDA-MB-468 cells through a bystander effect both in vitro and in vivo. In vivo efficacy studies have demonstrated that ADCs can effectively inhibit human tumor cell growth in immunodeficient mice. In addition to human tumor cell lines, the efficacy of ADCs was also studied in BALB/c-hPD1mice engrafted with murine colon cancer cells model, and the results showed that T-DXd and Keytruda could significantly inhibit CT26-hHER2 tumor growth, with the tumor growth inhibition rates being 57% and 66%, respectively, Tumor growth inhibition rate of the combined treatment group was about 91%, which was superior compared to the efficacy of single drug treatment. The evaluation of Fc segment-mediated effect (such as ADCC, CDC and ADCP) of ADCs antibodies was carried out in humanized immune system mouse models, such as NCG-hIL15 and NCG-hSGM3. In summary, we have established a drug evaluation platform using various humanized mouse models to study the synergistic anti-tumor effects of ADCs combined with immune checkpoint blockade antibodies. Citation Format: Fang Zhu, Hongyan Sun, Qian Lu, Yuan Fang, Jianming Xu, Cunxiang Ju, Hongyu Wang, Santi Suryani Chen, Zhiying Li, Mark Wade Moore, Jing Zhao, Xiang Gao. Efficacy evaluation by novel humanized mouse models for preclinical study of ADCs combined with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3242.
Abstract 2693: Combining ulixertinib (ERK1/2 Inhibitor) with EGFR and BRAF inhibition yields significant efficacy in preclinical <i>BRAF</i>V600E mutant colorectal cancer models
Authors: Deborah Knoerzer, Anupama Reddy, Jessica A. Box, Anna Groover, Brent Kreider, Martin Teresk, Caroline M. Emery
Journal: Cancer Research (2023)

Abstract The BRAFV600E mutation occurs in approximately 7% of colorectal cancer (CRC). BRAF plus EGFR inhibition (encorafenib with cetuximab) is an FDA approved treatment option for adult patients with metastatic CRC. Ultimately, patients develop resistance leading to disease progression. The addition of a MEK inhibitor, binimetinib, did not confer overall survival benefit. We hypothesized that addition of ERK inhibition would increase magnitude and duration of response to BRAF plus EGFR inhibition by overcoming MAPK-related acquired resistance mechanisms. ERK is the terminal master regulator kinase of the RAS-MAPK pathway, therefore, targeting this node is likely to be effective in the context of numerous mechanisms of acquired resistance that reactivate the MAPK pathway. Ulixertinib (BVD-523) is a first-in-class and best-in-class small molecule inhibitor of ERK1/2 currently being investigated in several oncology clinical trials, both as a single agent and in combination with other therapeutics. Ulixertinib has demonstrated efficacy in patients with tumors harboring alterations within the RAS-MAPK pathway. The efficacy of ulixertinib, in combination with EGFR inhibition (cetuximab), plus BRAF inhibition (encorafenib), was assessed in CRC cell line derived xenograft models harboring BRAFV600E mutations. The triple combination resulted in superior tumor growth inhibition compared to dosing of any single agent or doublet. RNA sequencing was performed on treated tumor samples. Expression of the mutant BRAF allele was readily confirmed from RNA sequencing data. Gene expression analysis showed differential expression of MAPK pathway genes in triple combination treated groups versus single or doublet therapy. A complete response to ulixertinib in combination with cetuximab and encorafenib for the treatment of a patient with metastatic CRC has been reported (Stuhlmiller, Timothy Joseph, et al. "Updated clinical outcomes from ULI-EAP-100, an intermediate expanded access program for ulixertinib (BVD-523)." (2022): e15101-e15101). The patient was treated under the Expanded Access Protocol (NCT04566393) for compassionate use access to ulixertinib. Preclinical data and clinical complete response warrants further investigation of ulixertinib plus BRAF and EGFR inhibition. Citation Format: Deborah Knoerzer, Anupama Reddy, Jessica A. Box, Anna Groover, Brent Kreider, Martin Teresk, Caroline M. Emery. Combining ulixertinib (ERK1/2 Inhibitor) with EGFR and BRAF inhibition yields significant efficacy in preclinical BRAFV600E mutant colorectal cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2693.
Abstract 4592: In vivo efficacy evaluation of anti-human VEGFR2 antibodies in humanized B-hVEGFR2 mice
Authors: Lei Zhao, Chengzhang Shang, Rebecca Soto, Chonghui Liu, Zhaoxue Yu
Journal: Cancer Research (2023)

Abstract Angiogenesis, vascular development, vascular permeability, and embryonic hematopoiesis is partly regulated by VEGF binding to VEGFR2, which forms receptor dimers and activates downstream signaling pathways. In addition to developmental cues, VEGFR2 is also associated with disease progression for psoriasis, rheumatoid arthritis, diabetic retinopathy, and more significantly, tumor growth and drug resistance. Given this, inhibition of VEGFR2 signaling is actively being studied, and multiple VEGFR2 inhibitors have entered various phases of clinical studies and have shown promising results. To evaluate in vivo efficacy of anti-human VEGFR2 antibodies, we generated a human VEGFR2 knock-in (B-hVEGFR2) mouse model by replacing the murine Vegfr2 extracellular domain sequence with the corresponding human sequence. We validated human VEGFR2 gene and protein expression in B-hVEGFR2 mice by RT-PCR and flow cytometry, respectively. Furthermore, we observed an anti-tumor effect with anti-human VEGFR2 antibodies in homozygous B-hVEGFR2 mice subcutaneously implanted with MC38 colon cancer cells. Taken together, we established a novel preclinical B-hVEGFR2 mouse model for evaluation of VEGFR2-targeted immunotherapy. Citation Format: Lei Zhao, Chengzhang Shang, Rebecca Soto, Chonghui Liu, Zhaoxue Yu. In vivo efficacy evaluation of anti-human VEGFR2 antibodies in humanized B-hVEGFR2 mice. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4592.
Abstract 6573: Drug Combo: A unique resource for efficient phase 1 trial design with manually curated MTD and DLT
Authors: Lei Wang, Shijun Zhang, Lai Wei, Yuxi Zhu, Dwight Owen, Lang Li
Journal: Cancer Research (2023)

Abstract Background: With prior preclinical and clinical knowledge, an effective and efficient cancer drug combinatory trial design could be expected, which will significantly accelerate the translation. However, our internal survey results suggested that some knowledge and data sources were underused or underestimated by researchers. One primary reason is that knowledge of drug combinations is scattered in many distinct databases. Moreover, some essential information, such as MTD and DLT was missed in current databases. Drug Combo (https://drugcombo.shinyapps.io/DrugCombo/) is the first knowledge base that integrates drug toxicity and PK data that may help design an efficient Phase I trial. It is a unique informatics resource that manually curated MTD and DLT are well organized in the knowledgebase. Methods: Drug Combo integrates various publicly available PK and toxicity knowledge for individual drugs and drug combinations (Table 1). More importantly, it is the unique knowledgebase with MTD and DLT. An expert team manually curated MTD and DLT data from publicly available publications. Some standard healthcare terminologies, e.g., Rx-Norm and MedDRA were applied for concept normalization at the back end. We implemented a User-Centered Design process in the user interface (UI).The UI layout will be simple, consistent, purposeful, comprehensive, easy to navigate and informative with readable or scannable typography. A summary view was provided to inform users of some essential information, e.g., MTD, and Overlapping toxicities. The user can find the details in the “Detail Tab”. Results: Currently, Drug Combo integrates pharmacological, pharmacokinetics, and toxicity data. Especially, MTD and DLT data from more than 1,600 published Phase I trials results are exclusive in Drug Combo. Conclusions: Drug Combo is the first knowledge base that provides expert-curated MTD and DLT information. It could help design an effective and efficient drug combination phase 1 trial. Current knowledge type in Drug Combo Type Source Drug Names DrugBank, RxNorm Target DrugBank, Manual curation Associated Condition RxNorm, Drug Labels PK profiles DrugBank Adverse Drug Event Drug Label, FAERS MTD Manual curation DLT Manual curation DDI DrugBank, Manual curation Citation Format: Lei Wang, Shijun Zhang, Lai Wei, Yuxi Zhu, Dwight Owen, Lang Li. Drug Combo: A unique resource for efficient phase 1 trial design with manually curated MTD and DLT [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6573.
Abstract 1931: Racialized economic segregation and late-stage colorectal cancer in the United States, 2009-2017
Authors: Eduardo J. Santiago-Rodriguez, Salma Shariff-Marco, Zinzi D. Bailey, Justin S. White, Isabel E. Allen, Robert A. Hiatt
Journal: Cancer Research (2023)

Abstract We examined the association between racialized economic segregation and late-stage colorectal cancer (CRC) in the US. All CRC cases diagnosed during 2009-2017 were identified using the United States Cancer Statistics database. Late-stage CRC was determined according to the presence of distant involvement (node or site) of the tumor at diagnosis. Racialized economic segregation was measured at the county level by the Index of Concentration at the Extremes (ICE) using the 2013-2017 American Community Survey estimates. ICE was calculated to show the extent to which individuals’ residence at diagnosis was located in counties of concentrated advantage or disadvantage. Four ICE metrics were considered, consisting of the difference between the number of households with White individuals reporting incomes in the top quintile of the US distribution, and the number of households with non-White (all people of color (POC), Black, Latino, or Asian American/Pacific Islander) individuals reporting incomes in the lowest quintile, divided by the total of households in the county. ICE metrics were further divided into quintiles based on the distribution of all counties in the US, with Q1 denoting least advantaged counties and Q5 the most advantaged counties. Multilevel logistic regression models accounting for clustering at counties were fit. Models were adjusted for age at diagnosis, sex, race and ethnicity, census region and year of diagnosis. A total of 1,356,053 cases were included in the analysis; mean age at diagnosis was 67 years, 52% were men, and 20% of cases had a late-stage diagnosis. In adjusted analysis for ICE-POC, people residing in less advantaged counties had higher odds of late-stage diagnosis than people residing in the most advantaged counties (OR [95% CI], Q1: 1.08 [1.05-1.11], Q2: 1.06 [1.04-1.09], Q3: 1.04 [1.02-1.07], Q4: 1.03 [1.01-1.05], Q5: reference). Similar results were observed on all four ICE metrics. Racialized economic segregation was associated with higher odds of late-stage CRC. These findings underscore the role of institutionalized racism as a driver of health inequities, with laws and policies that led to residential segregation also impacting the conditions for timely preventive care. Citation Format: Eduardo J. Santiago-Rodriguez, Salma Shariff-Marco, Zinzi D. Bailey, Justin S. White, Isabel E. Allen, Robert A. Hiatt. Racialized economic segregation and late-stage colorectal cancer in the United States, 2009-2017 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1931.
Abstract 197: Novel 3D bioprinted pancreatic tumor models for anti-cancer drug assessment
Authors: Shreyas Ramchandra Gaikwad, Sanjay K. Srivastava
Journal: Cancer Research (2023)

Abstract The advent of bioprinting as a technology has greatly advanced the field of tissue engineering. Currently, it is being explored as a means to create complex disease models, specifically to create 3D tissue environments. Such complex models allow incorporation of dynamic microenvironments during in vitro studies. In terms of cancer drug development, the tumor microenvironment plays a significant role in cancer progression. The use of conventional 2D models in the preliminary stages of anti-cancer screening reduces the probability of successful translation towards clinical trials. In the current study, we have utilized bioprinting to create pancreatic tumor model which specifically mimics the human pancreatic tissue in terms of the biological and chemical makeup. We developed and optimized a novel bioink to create the tumor tissue. The bioink specifically mimics the pancreatic tumor microenvironment making it unique and novel as compared to the currently used bioinks which are mainly derived from animal tissues. The bioprinted tumors were then characterized for various important hallmarks of cancer such as tissue stiffness, hypoxia, and immunomodulation. We also analyzed the expression of various oncoproteins in comparison to 2D cultures to explore the role of 3D microenvironments on cancer cells. Additionally, we evaluated the efficacy of standard chemotherapies, radiotherapy and immunotherapy on the tumors as compared to 2D monolayer cultures. The bioprinted pancreatic tumors serve as a bridge between conventional assays and animal models. We observed a stark difference in the characteristics of cancer cells in monolayer cultures versus bioprinted tumors. Since a bioprinter can be programmed to control the geometry of the tissue, these models serve as the most accurate means to mimic the in vivo tumor microenvironment. Citation Format: Shreyas Ramchandra Gaikwad, Sanjay K. Srivastava. Novel 3D bioprinted pancreatic tumor models for anti-cancer drug assessment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 197.
Abstract 6564: Biomarker discovery in triple negative breast cancer using RNA-sequencing analysis
Authors: Jenna B. Poulsen, Mauri E. Dobbs, Naomi Rapier-Sharman, Brett E. Pickett
Journal: Cancer Research (2023)

Abstract Breast cancer is the most common form of cancer, and afflicted over 2.2 million people in 2020. The sheer volume of patients who suffer from breast cancer warrants continued research and discovery efforts to improve treatment options. Triple negative breast cancer (TNBC) affects 10-15% of breast cancer patients. Unlike other forms of breast cancer, TNBC does not have estrogen or progesterone receptors and makes little to none of the HER2 protein. Due to the lack of these biomarkers that are typical treatment targets for other kinds of breast cancers, the hormone therapies and drugs that target other breast cancers are often ineffective against TNBC. This leaves chemotherapy and radiation therapy as the main treatment options. Although chemotherapy and radiation have treatment benefits, the recurrence rate after treatment is around 40%. Furthermore, these treatment options are very detrimental to the body, resulting in a weaker patient and a necessary recovery time between treatments. In this study, we analyzed publicly available RNA-sequencing (RNA-seq) data to identify the upregulated and downregulated transcriptional mechanism(s) that play a role in TNBC compared to healthy breast tissue. The analysis of the RNA-seq data was completed with the Automated Reproducible Modular Workflow for Preprocessing and Differential Analysis of RNA-seq Data (ARMOR), which trims, maps, and quantifies the mRNA sequencing reads to the human transcriptome for each read. The ARMOR program identified more than 12,000 differentially expressed genes between TNBC and healthy breast tissue samples. We then applied an artificial intelligence-based classification method, a random forest algorithm, to identify new biomarkers that best differentiate TNBC cells from healthy cells. These specific transcriptional biomarkers for TNBC could potentially be used as diagnostic biomarkers or as therapeutic targets. We anticipate that this data may expand the treatment options for TNBC. Citation Format: Jenna B. Poulsen, Mauri E. Dobbs, Naomi Rapier-Sharman, Brett E. Pickett. Biomarker discovery in triple negative breast cancer using RNA-sequencing analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6564.